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An Energy-Efficient Deep Convolutional Neural Network Inference Processor With Enhanced Output Stationary Dataflow in 65-nm CMOS

Title
An Energy-Efficient Deep Convolutional Neural Network Inference Processor With Enhanced Output Stationary Dataflow in 65-nm CMOS
Authors
Sim, JaehyeongLee, SominKim, Lee-Sup
Ewha Authors
심재형
Issue Date
2020
Journal Title
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
ISSN
1063-8210JCR Link

1557-9999JCR Link
Citation
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS vol. 28, no. 1, pp. 87 - 100
Keywords
Earth Observing SystemRadio frequencyEnergy consumptionSystem-on-chipMemory managementRegistersRandom access memoryConvolutional neural network (CNN)dataflowdeep learningenergy-efficient processornear-threshold voltage (NTV)
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
We propose a deep convolutional neural network (CNN) inference processor based on a novel enhanced output stationary (EOS) dataflow. Based on the observation that some activations are commonly used in two successive convolutions, the EOS dataflow employs dedicated register files (RFs) for storing such reused activation data to eliminate redundant memory accesses for highly energy-consuming SRAM banks. In addition, processing elements (PEs) are split into multiple small groups such that each group covers a tile of input activation map to increase the usability of activation RFs (ARFs). The processor has two different voltage/frequency domains. The computation domain with 512 PEs operates at near-threshold voltage (NTV) (0.4 V) and 60-MHz frequency to increase energy efficiency, while the rest of the processors including 848-KB SRAMs run at 0.7 V and 120-MHz frequency to increase both on-chip and off-chip memory bandwidths. The measurement results show that our processor is capable of running AlexNet at 831 GOPS/W, VGG-16 at 1151 GOPS/W, ResNet-18 at 1004 GOPS/W, and MobileNet at 948 GOPS/W energy efficiency.
DOI
10.1109/TVLSI.2019.2935251
Appears in Collections:
ETC > ETC
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